Forecasting Medium-Horizon Alzheimer's Disease Progression: Residual Gap-Aware Transformers for 24-Month CDR-SB Change from ADNI Clinical and Biomarker Histories

In three linesResidual gap-aware transformer model for predicting Alzheimer's disease progression over 24 months. Trained on 2,600 ADNI samples, it reduces MSE by 13.1% and increases prediction-observation correlation by 26.4% versus linear mixed-effects baseline, combining statistical reference with residual learning from irregular clinical and biomarker histories.

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